A labor-free index-guided semantic segmentation approach for urban vegetation mapping from high-resolution true color imagery  

作　　者：Peng Zhang Cong Lin Shanchuan Guo Wei Zhang Hong Fang Peijun Du 

机构地区：[1]Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology,Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources,School of Geography and Ocean Science,Nanjing University,Nanjing,People’s Republic of China [2]Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application,Nanjing,People’s Republic of China [3]c Nanjing Research Institute of Surveying,Mapping&Geotechnical Investigation,Co.Ltd,Nanjing,People’s Republic of China

出　　处：《International Journal of Digital Earth》2023年第1期1640-1660,共21页国际数字地球学报（英文）

基　　金：supported by the National Key R&D Program of China under Grant 2022YFC3800802;the National Natural Science Foundation of China under Grant 42271472;the National Natural Science Foundation of China under Grant 42201338;the program A for Outstanding PhD candidate of Nanjing University under Grant 202201A010;the Research Project of Nanjing Research Institute of Surveying,Mapping and Geotechnical Investigation,Co.Ltd under Grant 2021RD02.

摘　　要：Accurate and timely information on urban vegetation(UV)can be used as an important indicator to estimate the health of cities.Due to the low cost of RGB cameras,true color imagery(TCI)has been widely used for high spatial resolution UV mapping.However,the current index-based and classifier-based UV mapping approaches face problems of the poor ability to accurately distinguish UV and the high reliance on massive annotated samples,respectively.To address this issue,an index-guided semantic segmentation(IGSS)framework is proposed in this paper.Firstly,a novel cross-scale vegetation index(CSVI)is calculated by the combination of TCI and Sentinel-2 images,and the index value can be used to provide an initial UV map.Secondly,reliable UV and non-UV samples are automatically generated for training the semantic segmentation model,and then the refined UV map can be produced.The experimental results show that the proposed CSVI outperformed the existingfive RGB vegetation indices in highlighting UV cover and suppressing complex backgrounds,and the proposed IGSS workflow achieved satisfactory results with an OA of 87.72%∼88.16%and an F1 score of 87.73%∼88.37%,which is comparable with the fully-supervised method.

关 键 词：Urban vegetation mapping Sustainable Development Goals(SDGs) cross-scale vegetation index(CSVI) semantic segmentation high-resolution true color imagery(TCI) 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]